{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720496109664e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularizaion loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.794120407794253e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2,3,4,5],[3,4,5,6,7]])\n",
    "print(np.arange(0,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 16.24345364  -6.11756414  -5.28171752 -10.72968622]\n",
      " [  8.65407629 -23.01538697  17.44811764  -7.61206901]\n",
      " [  3.19039096  -2.49370375  14.62107937 -20.60140709]\n",
      " [ -3.22417204  -3.84054355  11.33769442 -10.99891267]\n",
      " [ -1.72428208  -8.77858418   0.42213747   5.82815214]]\n",
      "[0 1 2 2 1]\n",
      "b2 max relative error: 3.865028e-11\n",
      "W2 max relative error: 3.440708e-09\n",
      "b1 max relative error: 2.738422e-09\n",
      "W1 max relative error: 3.561318e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "print(X)\n",
    "print(y)\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.01714960793873202\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302966\n",
      "iteration 100 / 1000: loss 2.302729\n",
      "iteration 200 / 1000: loss 2.301247\n",
      "iteration 300 / 1000: loss 2.282601\n",
      "iteration 400 / 1000: loss 2.212519\n",
      "iteration 500 / 1000: loss 2.152256\n",
      "iteration 600 / 1000: loss 2.062875\n",
      "iteration 700 / 1000: loss 2.065117\n",
      "iteration 800 / 1000: loss 1.977815\n",
      "iteration 900 / 1000: loss 1.997275\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Clasification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can, with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302985\n",
      "iteration 100 / 1000: loss 2.302573\n",
      "iteration 200 / 1000: loss 2.298786\n",
      "iteration 300 / 1000: loss 2.275326\n",
      "iteration 400 / 1000: loss 2.204157\n",
      "iteration 500 / 1000: loss 2.139875\n",
      "iteration 600 / 1000: loss 2.071450\n",
      "iteration 700 / 1000: loss 2.078806\n",
      "iteration 800 / 1000: loss 1.949756\n",
      "iteration 900 / 1000: loss 2.057365\n",
      "iteration 0 / 1000: loss 2.302957\n",
      "iteration 100 / 1000: loss 2.273147\n",
      "iteration 200 / 1000: loss 2.051010\n",
      "iteration 300 / 1000: loss 2.018771\n",
      "iteration 400 / 1000: loss 1.864516\n",
      "iteration 500 / 1000: loss 1.939571\n",
      "iteration 600 / 1000: loss 1.847068\n",
      "iteration 700 / 1000: loss 1.754842\n",
      "iteration 800 / 1000: loss 1.768903\n",
      "iteration 900 / 1000: loss 1.758342\n",
      "iteration 0 / 1000: loss 2.302987\n",
      "iteration 100 / 1000: loss 2.161441\n",
      "iteration 200 / 1000: loss 1.900113\n",
      "iteration 300 / 1000: loss 1.811616\n",
      "iteration 400 / 1000: loss 1.724882\n",
      "iteration 500 / 1000: loss 1.627438\n",
      "iteration 600 / 1000: loss 1.779384\n",
      "iteration 700 / 1000: loss 1.558425\n",
      "iteration 800 / 1000: loss 1.658322\n",
      "iteration 900 / 1000: loss 1.807043\n",
      "iteration 0 / 1000: loss 2.302977\n",
      "iteration 100 / 1000: loss 2.044989\n",
      "iteration 200 / 1000: loss 1.839561\n",
      "iteration 300 / 1000: loss 1.821531\n",
      "iteration 400 / 1000: loss 1.731871\n",
      "iteration 500 / 1000: loss 1.679401\n",
      "iteration 600 / 1000: loss 1.721404\n",
      "iteration 700 / 1000: loss 1.496572\n",
      "iteration 800 / 1000: loss 1.606631\n",
      "iteration 900 / 1000: loss 1.513536\n",
      "iteration 0 / 1000: loss 2.302991\n",
      "iteration 100 / 1000: loss 1.957826\n",
      "iteration 200 / 1000: loss 1.734205\n",
      "iteration 300 / 1000: loss 1.645340\n",
      "iteration 400 / 1000: loss 1.697171\n",
      "iteration 500 / 1000: loss 1.618015\n",
      "iteration 600 / 1000: loss 1.561728\n",
      "iteration 700 / 1000: loss 1.493805\n",
      "iteration 800 / 1000: loss 1.489356\n",
      "iteration 900 / 1000: loss 1.584534\n",
      "iteration 0 / 1000: loss 2.303155\n",
      "iteration 100 / 1000: loss 2.302331\n",
      "iteration 200 / 1000: loss 2.296863\n",
      "iteration 300 / 1000: loss 2.263898\n",
      "iteration 400 / 1000: loss 2.178095\n",
      "iteration 500 / 1000: loss 2.095514\n",
      "iteration 600 / 1000: loss 2.022966\n",
      "iteration 700 / 1000: loss 1.995275\n",
      "iteration 800 / 1000: loss 1.959763\n",
      "iteration 900 / 1000: loss 2.016054\n",
      "iteration 0 / 1000: loss 2.303132\n",
      "iteration 100 / 1000: loss 2.251409\n",
      "iteration 200 / 1000: loss 2.091196\n",
      "iteration 300 / 1000: loss 1.932774\n",
      "iteration 400 / 1000: loss 1.862902\n",
      "iteration 500 / 1000: loss 1.822496\n",
      "iteration 600 / 1000: loss 1.718501\n",
      "iteration 700 / 1000: loss 1.813074\n",
      "iteration 800 / 1000: loss 1.634788\n",
      "iteration 900 / 1000: loss 1.705348\n",
      "iteration 0 / 1000: loss 2.303135\n",
      "iteration 100 / 1000: loss 2.106586\n",
      "iteration 200 / 1000: loss 1.961658\n",
      "iteration 300 / 1000: loss 1.963348\n",
      "iteration 400 / 1000: loss 1.728031\n",
      "iteration 500 / 1000: loss 1.766121\n",
      "iteration 600 / 1000: loss 1.751299\n",
      "iteration 700 / 1000: loss 1.576148\n",
      "iteration 800 / 1000: loss 1.560850\n",
      "iteration 900 / 1000: loss 1.615757\n",
      "iteration 0 / 1000: loss 2.303152\n",
      "iteration 100 / 1000: loss 2.010540\n",
      "iteration 200 / 1000: loss 1.968406\n",
      "iteration 300 / 1000: loss 1.722879\n",
      "iteration 400 / 1000: loss 1.738118\n",
      "iteration 500 / 1000: loss 1.718185\n",
      "iteration 600 / 1000: loss 1.590075\n",
      "iteration 700 / 1000: loss 1.552452\n",
      "iteration 800 / 1000: loss 1.637800\n",
      "iteration 900 / 1000: loss 1.507320\n",
      "iteration 0 / 1000: loss 2.303103\n",
      "iteration 100 / 1000: loss 1.933690\n",
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      "iteration 300 / 1000: loss 1.667321\n",
      "iteration 400 / 1000: loss 1.586558\n",
      "iteration 500 / 1000: loss 1.684408\n",
      "iteration 600 / 1000: loss 1.605066\n",
      "iteration 700 / 1000: loss 1.757676\n",
      "iteration 800 / 1000: loss 1.598680\n",
      "iteration 900 / 1000: loss 1.587772\n",
      "iteration 0 / 1000: loss 2.303227\n",
      "iteration 100 / 1000: loss 2.302547\n",
      "iteration 200 / 1000: loss 2.295837\n",
      "iteration 300 / 1000: loss 2.244178\n",
      "iteration 400 / 1000: loss 2.164128\n",
      "iteration 500 / 1000: loss 2.025646\n",
      "iteration 600 / 1000: loss 2.123716\n",
      "iteration 700 / 1000: loss 1.991945\n",
      "iteration 800 / 1000: loss 2.013146\n",
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      "iteration 0 / 1000: loss 2.303224\n",
      "iteration 100 / 1000: loss 2.238293\n",
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      "iteration 900 / 1000: loss 1.679129\n",
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      "iteration 700 / 1000: loss 1.529881\n",
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      "iteration 700 / 1000: loss 1.673848\n",
      "iteration 800 / 1000: loss 1.612461\n",
      "iteration 900 / 1000: loss 1.515956\n",
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      "iteration 400 / 1000: loss 2.191659\n",
      "iteration 500 / 1000: loss 2.079612\n",
      "iteration 600 / 1000: loss 2.130045\n",
      "iteration 700 / 1000: loss 2.001724\n",
      "iteration 800 / 1000: loss 2.059311\n",
      "iteration 900 / 1000: loss 1.930625\n",
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      "iteration 400 / 1000: loss 1.846818\n",
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      "iteration 600 / 1000: loss 1.817206\n",
      "iteration 700 / 1000: loss 1.625554\n",
      "iteration 800 / 1000: loss 1.795173\n",
      "iteration 900 / 1000: loss 1.691305\n",
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      "iteration 600 / 1000: loss 1.752582\n",
      "iteration 700 / 1000: loss 1.721891\n",
      "iteration 800 / 1000: loss 1.701672\n",
      "iteration 900 / 1000: loss 1.448523\n",
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      "iteration 600 / 1000: loss 1.520302\n",
      "iteration 700 / 1000: loss 1.525062\n",
      "iteration 800 / 1000: loss 1.479855\n",
      "iteration 900 / 1000: loss 1.652228\n",
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      "iteration 600 / 1000: loss 1.612945\n",
      "iteration 700 / 1000: loss 1.521556\n",
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      "iteration 900 / 1000: loss 1.466606\n",
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      "iteration 700 / 1000: loss 1.998693\n",
      "iteration 800 / 1000: loss 1.964666\n",
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      "iteration 700 / 1000: loss 1.648904\n",
      "iteration 800 / 1000: loss 1.624528\n",
      "iteration 900 / 1000: loss 1.742425\n",
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      "iteration 300 / 1000: loss 1.815077\n",
      "iteration 400 / 1000: loss 1.737954\n",
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      "iteration 600 / 1000: loss 1.566629\n",
      "iteration 700 / 1000: loss 1.724464\n",
      "iteration 800 / 1000: loss 1.646781\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 900 / 1000: loss 1.564674\n",
      "iteration 0 / 1000: loss 2.303493\n",
      "iteration 100 / 1000: loss 1.992063\n",
      "iteration 200 / 1000: loss 1.745975\n",
      "iteration 300 / 1000: loss 1.806479\n",
      "iteration 400 / 1000: loss 1.722162\n",
      "iteration 500 / 1000: loss 1.664620\n",
      "iteration 600 / 1000: loss 1.489102\n",
      "iteration 700 / 1000: loss 1.615872\n",
      "iteration 800 / 1000: loss 1.593065\n",
      "iteration 900 / 1000: loss 1.550960\n",
      "iteration 0 / 1000: loss 2.303475\n",
      "iteration 100 / 1000: loss 1.983266\n",
      "iteration 200 / 1000: loss 1.873430\n",
      "iteration 300 / 1000: loss 1.680705\n",
      "iteration 400 / 1000: loss 1.756646\n",
      "iteration 500 / 1000: loss 1.546453\n",
      "iteration 600 / 1000: loss 1.621349\n",
      "iteration 700 / 1000: loss 1.578665\n",
      "iteration 800 / 1000: loss 1.596538\n",
      "iteration 900 / 1000: loss 1.693239\n"
     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "input_size = 32 * 32 * 3\n",
    "hidden_size = [50,70,85,100,120]\n",
    "Learning_rate=[1e-4,3e-4,5e-4,7e-4,9e-4]\n",
    "num_classes = 10\n",
    "best_acc=-1\n",
    "for i in range(0,5):\n",
    "    for j in range(0,5):\n",
    "        net = TwoLayerNet(input_size, hidden_size[i], num_classes)\n",
    "        # Train the network\n",
    "        stats = net.train(X_train, y_train, X_val, y_val,\n",
    "                    num_iters=1000, batch_size=200,\n",
    "                    learning_rate=Learning_rate[j], learning_rate_decay=0.95,\n",
    "                    reg=0.25, verbose=True)\n",
    "        # Predict on the validation set\n",
    "        val_acc = (net.predict(X_val) == y_val).mean()\n",
    "        if val_acc>best_acc:\n",
    "            best_acc=val_acc\n",
    "            best_net=net\n",
    "#################################################################################\n",
    "#                               END OF YOUR CODE                                #\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.479\n"
     ]
    }
   ],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "*Your answer*:\n",
    "\n",
    "*Your explanation:*"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
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